Image guided radiosurgery method and apparatus using registration of 2D radiographic images with digitally reconstructed radiographs of 3D scan data

ABSTRACT

An image-guided radiosurgery method and system are presented that use 2D/3D image registration to keep the radiosurgical beams properly focused onto a treatment target. A pre-treatment 3D scan of the target is generated at or near treatment planning time. A set of 2D DRRs are generated, based on the pre-treatment 3D scan. At least one 2D x-ray image of the target is generated in near real time during treatment. The DRRs are registered with the x-ray images, by computing a set of 3D transformation parameters that represent the change in target position between the 3D scan and the x-ray images. The relative position of the radiosurgical beams and the target is continuously adjusted in near real time in accordance with the 3D transformation parameters. A hierarchical and iterative 2D/3D registration algorithm is used, in which the transformation parameters that are in-plane with respect to the image plane of the x-ray images are computed separately from the out-of-plane transformation parameters.

FIELD OF THE INVENTION

The present invention relates to radiosurgery. In particular, the present invention relates to an image guided radiosurgery method and apparatus using 2D/3D image registration.

BACKGROUND

Radiosurgery is used to treat tumors and other lesions by delivering a prescribed high dose of high-energy radiation to the target area while minimizing radiation exposure to the surrounding tissue. In radiosurgery, precisely focused beams of radiation (e.g. very intense x-ray beams) are delivered to a target region in order to destroy tumors or to perform other types of treatment. The goal is to apply a lethal amount of radiation to one or more tumors, without damaging the surrounding healthy tissue. Radiosurgery therefore calls for an ability to accurately target a tumor, so as to deliver high doses of radiation in such a way as to cause only the tumor to receive the desired dose, while avoiding critical structures such as the spinal cord.

Conventional radiosurgery uses a rigid and invasive stereotactic frame to immobilize the patient prior to diagnostic CT or MRI scanning. The treatment planning is then conducted from the diagnostic images. The treatment planning software determines the number, intensity, and direction of the radiosurgical beams that should be cross-fired at the target, in order to ensure that a sufficient dose is administered throughout the tumor so as to destroy it, without damaging adjacent healthy tissue. Radiation treatment is typically accomplished on the same day treatment planning takes place. Immobilization of patient is necessary in order to maintain the spatial relationship between the target and the radiation source to ensure accurate dose delivery. The frame is fixed on the patient during the whole treatment process.

Image-guided radiosurgery eliminates the use of invasive frame fixation during treatment, by frequently and quasi-continuously correcting patient position or aligning radiation beam with the patient target. To correct patient position or align radiation beam, the patient pose during treatment needs to be detected. This is accomplished by registering the X-ray image acquired at the treatment time with the diagnostic 3D scan data (e.g., CT, MRI, or PET scan data) obtained pre-operatively at the time of treatment planning. The positions of the target are defined by physicians at the time of treatment planning, using the diagnostic 3D scan. The 3D scan data are used as a reference, in order to determine the patient position change during treatment. Typically, digitally reconstructed radiographs (DRRs) are generated from the 3D scan data, and are used as 2D reference images. Similarity measures are used to compare the image intensities in the x-ray images and the DRR images, in order to determine the change in the position of the patient and the treatment target. In the field of medical image registration, this problem is categorized as a 2D/3D registration.

Image-guided radiosurgery requires precise and fast positioning of the target at the treatment time. In practice, the accuracy should be below 1 mm, and the computation time should be on the order of a few seconds. Unfortunately, it is difficult to meet both requirements simultaneously, because of a number of reasons. First, the two different image modalities (CT and x-ray) are characterized by different spatial resolution and image quality. X-ray image resolution and quality are generally superior to the resolution and quality of DRR images, which are only synthesized projection images. Second, DRR generation relies on a proper attenuation model. Because attenuation is proportional to mass intensity, the exact relationship between mass intensity and CT image intensity needs to be known for an accurate modeling. It is difficult to establish this relationship, however, so the linear attenuation model is often used. The skeletal structures in DRR images cannot be reconstructed very well when the linear model is used. Finally, x-ray images usually have a large image size (512×512). It is desirable to use full resolution images, for better registration accuracy. However, the full resolution of the x-ray images is rarely used, because of the extremely slow computation time that results from such use.

The methods used in the 2D/3D registration can be categorized into two types. The first type of methods is based on image features. The image features could be anatomical edges, for example, or segmented objects. The registration accuracy depends on the accuracy of edge detection or object segmentation. The main advantage of this type of method is the high computation speed. Because the full information content of the image is not used, however, accuracy is sacrificed. The second type of method is based on image intensity content. The original images are used for registration. Usually, a good accuracy can be achieved. Because a long time computation is required, however, image intensity based methods are not practical for radiosurgery, or for clinical practice in general.

U.S. Pat. No. 5,901,199 by Murphy et al. (the “Murphy patent”) describes a high-speed inter-modality image registration via iterative feature matching. The Murphy patent is a feature-based method. Prior to treatment, extraction and segmentation of silhouettes of the patient's skull are performed in order to make a feature mask. A set of DRR images are generated from the 3D CT data and are then masked, in order to isolate key pixels that are associated with anatomical edge features. The masked image contains only 5%-10% of the total image pixels. During treatment, the acquired x-ray images are similarly masked. The registration is conducted on the masked DRRs and the masked X-ray images. The registration process is completed in a few seconds. However, the accuracy and stability of the estimates are not sufficient to meet the sub-mm precision that is required in radiosurgery applications.

For these reasons, there is a need for a method and system in image-guided radiosurgery for tracking the position of the treatment target, throughout the radiosurgical treatment procedure, with a computation time that is fast enough for purposes of radiosurgery, while at the same time maintaining sufficient accuracy and stability.

SUMMARY OF THE INVENTION

The present invention is directed to an accurate and rapid method and system for tracking target position for image guided radiosurgery. A hierarchical and iterative 2D/3D registration algorithm is used that allows for accurate and rapid correction of target position, and alignment of radiosurgical beams, throughout the treatment procedure.

A method in image guided radiosurgery is presented for aligning the position of a treatment target relative to a radiosurgical beam generator during treatment. The method includes generating a pre-treatment 3D scan of the target, which is treated as a rigid body with six degrees of freedom for describing its position. The 3D scan (for example a CT scan, an MRI scan, or a PET scan) shows the position of the target at treatment planning time. One or more 2D x-ray images of the target are generated at near real time. The x-ray images show the position of the target at a current time during treatment. Preferably, two orthogonal x-ray projections images are generated, using imaging beams having a known position, angle, and intensity. A set of 2D reconstructed images, preferably DRRs (digitally reconstructed radiographs), are generated offline, based on the 3D scan data and the known positions, angles, and intensities of the imaging beams.

The DRRs are registered with the x-ray images, using a hierarchical and iterative framework. The 3D rigid body transformation parameters, which represent the change in position of target between said 3D scan and the x-ray images, are generated. The registration is performed for each projection individually, and the results are subsequently combined. In-plane and out-of-plane transformation parameters are estimated using different search methods, including 3D multi-level matching and 1D searching, then iteratively refined until a desired accuracy is reached. The relative position of the radiosurgical beams and the target are continuously adjusted in near real time, throughout the treatment, in accordance with the 3D transformation parameters obtained via the 2D/3D registration process.

An image guided radiosurgical system is presented for radiosurgical treatment of a target, such as a human skull or other target region within a patient's anatomy. The system includes means for generating pre-treatment 3D scan data of the target, for example a CT scanner or an MRI system. A radiosurgical beam generator is provided for generating at least one radiosurgical beam for treating the target. Imaging means are provided for generating 2D x-ray images of the target in near real time. The imaging means include one or more (preferably two) imaging x-ray beam sources for generating at least one imaging beam having a known intensity, position and angle. The imaging beams are directed toward and through the target, and detected by corresponding image receivers (cameras) that detect the beams after the beams have traversed the target. The detection signals are processed by an image processor, which generates the x-ray images. Preferably, a pair of x-ray sources and a corresponding pair of x-ray cameras are provides, so that two orthogonal x-ray projection images are generated.

Means are provided for generating a set of 2D DRRs for each x-ray projection image. The DRRs are based on the 3D scan data, and generated using the known intensity, location, and angle of the imaging beams. Processing means are provided for registering the DRRs with the x-ray images. The processing means include a processor for computing a set of 3D transformation parameters that represent the change in position of the target between the 3D scan and the near real time x-ray images. The processor includes software for performing a 2D/3D registration algorithm that includes estimating in-plane and out-of-plane transformation parameters for each projection, using a number of search methods including 3D multi-level matching, 2D sub-pixel matching, and 1D searching, and using two different similarity methods (sum-of-square differences and pattern intensity) at different phases of the registration process. The radiosurgical system also including positioning means, responsive to a controller, for adjusting in near real time the relative position of the radiosurgical beams and the target, in accordance with the 3D transformation parameters obtained by the 2D/3D registration process.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates the geometric relations between a 3D treatment target and two orthogonal x-ray projections, in an image-guided radiosurgery method and system in accordance with one embodiment of the present invention.

FIG. 2 is a schematic diagram of a methodology for tracking a treatment target during image-guided radiosurgery, in accordance with one embodiment of the present invention.

FIG. 3 schematically illustrates an image-guided radiosurgery system, constructed in accordance with one embodiment of the present invention.

DETAILED DESCRIPTION

The present invention features an accurate and rapid method and system for tracking the position of a treatment target in image-guided radiosurgery. The position of the target is tracked in six degrees of freedom. The present invention is useful for patient position correction and radiation beam alignment during radiosurgery/radiotherapy of a treatment target, for example a tumor within the brain or skull. The method and system of the present invention allows for a fully automatic tracking process, with no need for user interaction.

In overview, 2D reconstructed radiographs (typically, DRRs) are generated offline from a pre-treatment 3D scan. The 3D scan is typically a CT scan, however, any other 3D diagnostic scan (such as MRI or PET) may also be used. The DRRs are used as references for determining the position of the target. X-ray images are then acquired in near real time during treatment, in order to determine the change in target position. A hierarchical and iterative framework is used to register between the DRRs and X-ray images. The relative position of the radiosurgical beams and the target is continuously adjusted in near real time, in accordance with the results of the registration process, so that the radiosurgical beams remain properly focused onto the target throughout the treatment. By using different search methods for different transformation parameters, and by optimizing similarity measures for different phases of the registration procedure, an increased accuracy is achieved in the target tracking process, with a significantly reduced computing time.

In the present invention, the radiosurgery target is treated as a rigid body. As well known, a rigid body is defined as an object whose internal geometric relationships remain static or unchanged over time. Because there are no external forces imposed on a radiosurgical target during radiation treatment, it is reasonable to treat the target as a rigid body, and to use a 3D rigid transformation for the registration process. The 3D rigid transformation is described using six degrees of freedom: three translations along three mutually orthogonal axes in a 3D scan coordinate system (conventionally labeled as the x-, y-, and z-axes), and three rotations (roll, pitch, yaw) about these three axes. The six degrees of freedom are thus represented by six 3D transformation parameters: (x, y, z, r, p, w), where r represents the rotation about the x-axis, p represents the rotation about the y-axis, and w represents the rotation about the z-axis.

In the present invention, two orthogonal x-ray projections are used to solve for these six parameters. FIG. 1 illustrates the geometric relations between a three-dimensional treatment target, and two orthogonal 2D x-ray projections (labeled A and B in FIG. 1), in an image-guided radiosurgery method and system in accordance with the present invention. Cameras (or image receivers) A and B receive their x-ray projections from respective x-ray sources (not shown). In the 3D scan coordinate system, the x-axis is directed inward into the paper, and is not indicated in FIG. 1. As explained above, in the 3D coordinate system, the change in position of the target is represented by three translations and three rotations (x, y, z, r, p, w).

In FIG. 1, the orthogonal 2D projections A and B are viewed from the directions o_(A)s_(A) and o_(B)s_(B), respectively. For each of the projections A and B, FIG. 1 illustrates respective 2D planar coordinate systems that are fixed with respect to the image plane that characterizes each projection. The image planes A and B for the projections A and B are thus defined by mutually orthogonal axes within the respective coordinate systems. These axes are shown in FIG. 1 as (x_(A), y_(A)) for projection A, and (x_(B), y_(B)) for projection B. The direction of the axis x_(A) in the 2D coordinate system for projection A, and the direction of the x-axis in the 3D scan coordinate system, are opposite with respect to each other. The direction of axis x_(B) in the coordinate system for projection B, and the direction of the axis x in the 3D scan coordinate system, are the same.

As shown in FIG. 1, each projection is characterized by a respective set of transformation parameters, namely (x_(A), y_(A), θ_(A), r_(A), φ_(A)) for projection A, and (x_(B), y_(B), θ_(B), r_(B), φ_(B)) for projection B. The two out-of-plane rotations (with respect to the image plane) in projections A and B are denoted by (r_(A), φ_(A)) and (r_(B), φ_(B), respectively, where r denotes the amount of rotation about the x-axis (in the 3D scan coordinate system), and φ denotes the amount of rotation about the o_(A)s_(A) axis (for projection B) or the o_(B)s_(B) axis (for projection A). The in-plane translations and rotation in projections A and B are denoted (x_(A)y_(A)θ_(A)) and (x_(B)y_(B)θ_(B)), respectively. As easily seen, (x_(A) y_(A)) and (x_(B) y_(B)) denote the amount of translations within the image planes for each projection (A and B) in the directions of the x- and y-axes that define each image plane (x_(A)- and y_(A)- for projection A, and x_(B)- and y_(B) for projection B), while θ_(A) and θ_(B) denote the amount of rotation within each image plane about an axis (not shown) that is perpendicular to both the x_(A)-(or x_(B)-) and y_(A)-(or y_(B)-) axes.

As can be seen from FIG. 1, the out-of-plane rotation (φ_(A) in projection A is the same as the in-plane rotation θ_(B) in projection B, and the out-of-plane rotation φ_(B) in projection B is the same as the in-plane rotation θ_(A) in projection A. The use of the two projections A and B thus over-constrains the problem of solving for the six degrees of freedom. As seen from FIG. 1, x_(A)=x_(B), r_(A)=r_(B), θ_(A)=φ_(B) and θ_(B)=φ_(A).

FIG. 2 is a schematic diagram of a methodology for tracking a treatment target during image-guided radiosurgery, in accordance with one embodiment of the present invention. In overview, two sets of DRRs (or other 2D reconstructed images, for example) are generated in the present invention, one set for each of the projections A and B. The process of generating DRRs is carried out after the radiation treatment planning is completed, but before treatment delivery. Before patient treatment, DRR initialization is performed on the initial DRRs, to create a set of in-plane rotated reference DRR images. In the course of radiosurgical treatment, the real time x-ray projection images are acquired and pre-processed. The processed x-ray images for each projection are registered with the corresponding set of reference DRR images. The results of the registration, (x_(A), y_(A), θ_(A), r_(A), φ_(A)) and (x_(B), y_(B), θ_(B), r_(B), φ_(B)) for projections A and B, are combined to produce the final six rigid transformation parameters (x, y, z, r, p, w).

The step of generating DRRs is performed offline, and involves specifying a set of rotation angles for each of the out-of-plane rotations r and φ, for each projection A and B. Each set of DRRs for each projection includes DRRs that correspond to different combinations of these out-of-plane rotation angles. Therefore, the total number of DRR images is therefore N_(r)* N_(φ), where N_(r) and N_(φ) respectively denote the number of rotation angles for the two out-of-plane rotations r and φ. Because the out-of-plane rotations are expected to approach zero after patient alignment, the angles are more densely sampled in the range close to zero, and more sparsely sampled in the range of larger rotation angles.

Once the DRRs for out-of-plane rotations are generated, DRR initialization is performed, by computing a set of in-plane rotated reference DRRs images (in 0 degree). The most intensive part of computation in registration is the in-plane rotation computation. To achieve a fast computation, therefore, it is desirable to compute as many as possible in-plane rotations for the reference DRRs, before the registration process. All the reference DRR images are stored in a memory unit in the radiosurgical system, and are used for registration in each x-ray image acquisition, during target alignment and treatment.

The 2D x-ray images of the target that represent the two orthogonal projections A and B onto the respective image planes (shown in FIG. 1) are acquired in near real time, by transmitting respective imaging beams through the target and onto the cameras A and B. The imaging beams for each projection (attenuated by virtue of having passed through the target) are detected by the respective cameras, after passing through the target. The detection signals are sent to an image sensor, so that the 2D x-ray projection images are generated. The raw 2D x-ray images are pre-processed, before beginning the 2D/3D registration process. Pre-processing the raw x-ray images is necessary, in order to make the x-ray and DRR images have the same orientation, same image size, and same bit depth.

For projection A, given a set of reference DRR images which correspond to different combinations of the two out-of-plane rotations (r_(A), φ_(A)), the 2D in-plane transformation (x_(A), y_(A), φ_(A)) can be estimated by the 2D image comparison. Determining the two out-of-plane rotations (r, θ_(B)) relies on which reference DRR is used for an optimal similarity match. Similarly, the 2D in-plane transformation (x_(B), y_(B), φ_(B)) and the out-of-plane rotations (r_(B), φ_(B)) can be estimated for projection B. Given the results (x_(A), y_(A), θ_(A), r_(A), φ_(A)) for projection A and (x_(B), y_(B), θ_(A), r_(B), (φ_(B)) projection B, the 3D transformation can be obtained by the following expressions x=(x _(A) +x _(B))/2, y=(y _(A) −y _(B))/{square root}{square root over (2)}, z=(y _(A) +y _(B))/{square root}{square root over (2)}, r=(r _(A) +r _(B))/2, p=(θ_(B)−θ_(A))/{square root}{square root over (2)}, w=(θ_(B)+θ_(A))/{square root}{square root over (2)}

In the present invention, the 2D/3D registration process uses a registration algorithm that is designed in a unique hierarchical and iterative framework.

The 2D/3D registration process is described in detail in co-pending patent application (submitted on the same day as the current application, and identified by attorney docket number ACCL-125), which is owned by the assignee of the present application and hereby incorporated by reference in its entirety. The similarity measures used in this 2D/3D registration process is described in detail in co-pending patent application (submitted on the same day as the current application, and identified by attorney docket number ACCL-127), which is owned by the assignee of the present application and hereby incorporated by reference in its entirety.

It has been observed that the out-of-plane rotations can be detected with a good accuracy only after the in-plane parameters have already been well estimated. It has also been found that the out-of-plane rotations are able to safely converge to the correct values when starting from the nominal position of the DRRs. Accordingly, a separate computation is carried out for the out-of-plane versus in-plane transformation parameters, during the registration process: the two out-of-plane rotations (r, φ) are estimated from the exact reference DRR images, while the in-plane transformation parameters (x, y, θ) are computed directly from the 2D images. The in-plane parameters (x, y, θ) are first computed using the nominal reference DRRs. An initial estimate for the out-of-plane rotations (r, φ) is then carried out, based on the previously obtained values of the in-plane transformation parameters (x, y, θ). In order to maximize efficiency and accuracy, different search strategies are used for estimating the out-of-plane transformations and the in-plane transformations, respectively. Also, multiple similarity measure criteria are used that have been optimized at the different phases during the registration.

In an exemplary embodiment of the present invention, the registration process is described in terms of six distinct phases. In phase 1, the in-plane transformation parameters (x, y, θ) are initially estimated using the in-plane rotated DRR images, which are generated offline from the nominal reference DRR. Using a sum of absolute differences method (SAD; described in the co-pending ACCL-127 application) as the similarity measure, the three parameters are rapidly searched by a 3D multi-level matching method. In this phase, there is no floating computation. The pixel accuracy for translations and half-degree accuracy for in-plane rotation are achieved.

In phase 2, based on the values of (x, y, θ) determined in the first phase described above, the two out-of-plane rotations (r, φ) are separately searched in one dimension. A more complicated similarity measure, based on pattern intensity, is used to detect the reference DRR image that corresponds to a combination of the two out-of-plane rotations (r,φ). The search space for the possible rotation angles is the full search range of out-of-plane rotation angles. As the initial estimation, the full search range is sampled at every one-degree interval.

In phase 3, the in-plane translation parameters (x, y) are refined using 2D sub-pixel matching. In phase 4, the in-plane rotation parameter θ is refined using 1D interpolation, based the updated values for the in-plane translation parameters (x, y) and the out-of-plane rotation parameters (r, φ). In phase 5, the out-of-plane rotations are separately refined to a better accuracy using a 1D search, based the updated in-plane transformation (x, y, θ). In phases 3, 4, and 5, similarity measure method based on pattern intensity is used, to ensure a higher accuracy.

Phases 3, 4 and 5 are iteratively repeated until a sufficient accuracy is obtained. Once the desired accuracy is reached, the final out-of-plane rotations are 1-D interpolated, in the final phase 6 of the registration process. In practice, a high accuracy is obtained for both translations and rotations, using the method and system of the present invention. For translations, an accuracy of 0.5 mm or better is reached, and for rotations an accuracy of 0.5 degrees or better is reached. The total computing time is a few seconds, which is an order of magnitude faster than other methods in the prior art.

FIG. 3 schematically illustrates an image-guided radiosurgery system, constructed in accordance with one embodiment of the present invention. In overview, the image guided radiosurgery system 100 includes a means 101 for generating pre-treatment 3D scan data of the target; radiosurgical beam generator 102; a positioning system 104; imaging means 106; and a controller 108. The system 100 may also include an operator control console and display 140. The means 101 may be a CT scanner, for example, or an MRI system or a PET system.

The radiosurgical beam generator 102 generates, when activated, a plurality of collimated radiosurgical beam (e.g. x-ray beam). The cumulative effect of the radiosurgical beams, when properly directed to and focused onto the target, is to necrotize or perform other treatments in a target within the patient's anatomy. The positioning system 104 may be an industrial robot, by way of example, and the beam generator 102 may be a small x-ray linac mounted to an arm of the industrial robot 104.

The imaging means 106 is preferably an x-ray imaging system for generating a pair of orthogonal x-ray projection images of the target. The imaging means 106 preferably has a pair of x-ray sources for generating diagnostic imaging beams (having known positions, angles, and intensities), and a corresponding pair of x-ray image detectors which detect the beams after the beams have passed through the target.

The controller 108 includes software for generating a set of reconstructed 2D images (preferably DRRS) of the target, based on the 3D scan data from the 3D scanner 101, and the known intensity, location, and angle of the imaging beams. The controller 108 includes software for registering the DRRs with the real time x-ray images. The registration software is able to compute a set of 3D transformation parameters that represent the change in position of the target between the 3D scan and the near real-time x-ray images.

The positioning system 104 is responsive to commands from the controller 108, to continuously adjust, in near real time, the relative position of the radiosurgical beam generator and the target by the amount prescribed by the 3D transformation parameters obtained through the registration process.

The method and system of the present invention provide numerous advantages over the prior art. In the present invention, for example, a fully automatic tracking process is achieved, and no user intervention is necessary. Also, the registration process is optimized to allow the use of images having a full resolution. In this way, the full informational content of each image can be utilized, without having to leave out any image features. These results are achieved while reducing the total computation time for the tracking process to a few seconds, which is an order of magnitude faster than the existing prior art methods. At the same time, a high accuracy is achieved for both the translation parameters (below about 0.5 mm) and the rotation parameters (below about 0.5 degrees).

While the invention has been particularly shown and described with reference to specific preferred embodiments, it should be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the spirit and scope of the invention as defined by the appended claims. 

1. A method in image guided radiosurgery for aligning the position of a treatment target relative to a radiosurgical beam generator during treatment, the method comprising: a. generating a pre-treatment 3D scan showing the position of said target at treatment planning time; b. generating a set of 2D reconstructed images from said 3D scan; c. generating at near real time one or more 2D x-ray images of said target, wherein said x-ray images show the position of said target at a current time during treatment; d. registering said reconstructed images with said x-ray images by computing a set of 3D transformation parameters that represent the change in position of target between said 3D scan and said x-ray images; and e. in near real time, adjusting the relative position of said radiosurgical beam generator and said target by the amount prescribed by said 3D transformation parameters computed in step d; wherein said target is allowed six degrees of freedom of position.
 2. A method in accordance with claim 1, wherein said 3D transformation parameters represent the difference between the position of the target at said treatment planning time, and the position of the target at said current time.
 3. A method in accordance with claim 1, further comprising repeating steps c through e quasi-continuously during treatment, whereby one or more radiosurgical beams generated by said beam generator remain properly focused onto said target throughout said radiosurgical treatment.
 4. A method in accordance with claim 1, further comprising the step of creating a treatment plan after step a and before step b.
 5. A method in accordance with claim 4, wherein said treatment plan specifies the number, intensity, and direction of said one or more radiosurgical beams that are required in order to administer a sufficient radiation dose to said target while minimizing the radiation to adjacent tissue.
 6. A method in accordance with claim 1, further comprising the step of processing said x-ray images, after step c and before step d, so as to match the orientation, image size, and bit depth of said x-ray images with the orientation, image size, and bit depth of said reconstructed 2D images.
 7. A method in accordance with claim 1, wherein said x-ray images are generated by transmitting one or more x-ray imaging beams through said target, said imaging beams having a known intensity, position, and angle; and wherein said 2D reconstructed images are DRRs (digitally reconstructed radiographs) that represent the synthesized radiographic image of said target that would be obtained with said imaging beams at said known intensity and from said known positions and angles, if said target were positioned in accordance with said pre-treatment 3D scan.
 8. A method in accordance with claim 1, wherein said 3D transformation parameters are 3D rigid body transformation parameters, and wherein said 3D transformation parameters are represented by three translations and three rotations (x, y ,z, r, p, w); wherein x, y, z represent the translations of said target in the directions of three mutually orthogonal axes, respectively, and wherein r, p, w represent three rotations (roll, pitch, yaw) about said three orthogonal axes.
 9. A method in accordance with claim 1, wherein said x-ray images generated in step c comprises x-ray projection images that represent at least two orthogonal projections A and B of said target onto respective projection image planes, said x-ray projection images being formed by transmitting at least two x-ray imaging beams through said target and onto said respective image planes, wherein each imaging beam is received by a respective x-ray camera after passing through said target.
 10. A method in accordance with claim 9, wherein step b of generating reconstructed images comprises: generating two sets of reconstructed images, one set for each of said projections A and B.
 11. A method in accordance with claim 10, wherein step d of registering said reconstructed images with said x-ray images comprises: A. individually registering each x-ray projection image A and B with their respective set of reconstructed images, by determining a separate set of transformation parameters for each projection x-ray image; and B. combining the resulting parameters for each projection to obtain said 3D transformation parameters.
 12. A method in accordance with claim 11, wherein said transformation parameters for each of said projections A and B are described by two out-of-plane rotational parameters (r_(A), φ_(A)) and (r_(B), φ_(B)) respectively, and by three in-plane transformation parameters (x_(A), y_(A), θ_(A)) and (x_(B), y_(B), θ_(B)), respectively.
 13. A method in accordance with claim 12, wherein said 2D reconstructed images are DRRs, and wherein step b of generating said 2D reconstructed images comprises: i) for each projection, specifying a set of rotation angles for each of said out-of-plane rotation parameters r and φ, N_(r) being the number of rotation angles for rotation parameter r, and Nφ being the number of rotation angles for rotation parameter φ; and ii) generating two sets of DRRs, one set for each of said projections A and B; wherein each set includes DRRs that correspond to different combinations of said out-of-plane rotation angles, so that the number of DRRs in each set is N_(r)*N_(φ).
 14. A method in accordance with claim 13, wherein the step of generating 2D reconstructed images further comprises the step of computing a set of in-plane rotated DRR images by performing a plurality of in-plane rotations on said DRRs, thereby creating a set of in-plane rotated reference DRRs for each projection.
 15. A method in accordance with claim 14, wherein said step of creating reference DRRs is performed offline.
 16. A method in accordance with claim 9, wherein the step of computing said 3D transformation parameters comprises: i) individually computing the transformation parameters (x_(A), y_(A), θ_(A)) and (x_(B), y_(B), θ_(B)) for each projection image A and B; and ii) combining the transformation parameters for projection A with the transformation parameters for projection B so as to obtain said 3D transformation parameters; and wherein said 3D transformation parameters are represented by three translations and three rotations (x, y, z, r, p, w).
 17. A method in accordance with claim 16, wherein said 3D transformation parameters are related to the transformation parameters for projections A and B by the following relationship: x=(x _(A) +x _(B))/2, y=(y _(A) −y _(B))/{square root}{square root over (2)}, z=(y _(A) +y _(B))/{square root}{square root over (2)}, r=(r _(A) +r _(B))/2, p=(θ_(B)−θ_(A))/{square root}{square root over (2)}, w=(θ_(B)+θ_(A))/{square root}{square root over (2)}
 18. A method in accordance with claim 16, wherein the step of computing the transformation parameters for each projection comprises: i) computing the in-plane transformation parameters using said in-plane rotated reference DRRs; and thereafter ii) estimating the out-of-plane rotation parameters using the in-plane transformation parameters computed in step i) above; and thereafter iii) iteratively refining said in-plane and out-of-plane transformation parameters, until said parameters converge to a sufficient accuracy.
 19. A method according to claim 18, wherein step i) is performed using 3D multi-level matching, and a sum of absolute difference similarity measure.
 20. A method according to claim 18, wherein step ii) is performed using a 1D search and a pattern intensity similarity measure.
 21. A method according to claim 20, wherein step iii) comprises: a) refining the in-plane translation parameters x and y using 2D sub-pixel matching; and thereafter b) refining the in-plane rotation parameter using 1-D interpolation,
 22. A method in accordance with claim 1, wherein said 3D scan comprises at least one of: a CT scan, an MRI scan, an ultrasound scan, and a PET scan.
 23. An image guided radiosurgical system for radiosurgical treatment of a target, the system comprising: a. means for providing pre-treatment 3D scan data of said target; b. a radiosurgical beam generator for generating at least one radiosurgical beam; c. imaging means for generating one or more 2D x-ray images of said target in near real time, said imaging means including: i) an imaging beam source for generating at least one imaging beam having a known intensity, and having a known position and angle relative to said target; and ii) means for directing said imaging beam towards and through said target from said known location and angle, and at said known intensity; iii) at least one image receiver for detecting the attenuated imaging beam after said beam has passed through said target; and iv) an image processor for processing data from said image receiver to generate said x-ray image; d. a controller, including: i) means for generating at least one reconstructed 2D image of,said target, based on said 3D scan data, and using said known intensity, location, and angle of said imaging beam; ii) registration means for registering said reconstructed 2D image with said near real time x-ray image, said registration means including means for computing a set of 3D transformation parameters that represent the change in position of said target between said 3D scan and said near real time x-ray image; and e. positioning means; responsive to said controller, for adjusting in near real time the relative position of said radiosurgical beam generator and said target by the amount prescribed by said 3D transformation parameters.
 24. A system in accordance with claim 23, wherein said 2D reconstructed images comprises DRRs.
 25. A system in accordance with claim 23, wherein said 3D scan data comprise at least one of CT scan data, MRI scan data, and PET scan data.
 26. A system in accordance with claim 23, wherein said one or more 2D x-ray images of said target comprise x-ray projection images that represent at least two orthogonal projections A and B of said target onto respective projection image planes, and wherein said x-ray projection images are formed by transmitting at least two x-ray imaging beams through said target and onto said respective image planes, wherein each imaging beam is received by a respective x-ray camera after passing through said target.
 27. A system in accordance with claim 23, wherein said means for generating at least one reconstructed 2D image comprises means for generating two sets of reconstructed images, one set for each of said projections A and B.
 28. A system in accordance with claim 23, wherein said registration means comprises: A. means for individually registering each x-ray projection image A and B with their respective set of reconstructed images by determining a separate set of transformation parameters for each projection x-ray image; and B. combining the resulting parameters for each projection to obtain said 3D transformation parameters.
 29. A system in accordance with claim 28, wherein said transformation parameters for each of said projections A and B are described by two out-of-plane rotational parameters (r_(A), φ_(A)) and (r_(B), φ_(B)) respectively, and by three in-plane transformation parameters (x_(A), y_(A), θ_(A)) and (x_(B), y_(B), θ_(B)), respectively.
 30. A system in accordance with claim 29, wherein said means for generating at least one reconstructed 2D image of said target comprises: i) means for specifying, for each projection A and B, a set of rotation angles for each of said out-of-plane rotation parameters r and φ, wherein the number of rotation angles for rotation parameter r is Nr, and the number of rotation angles for rotation parameter φ is Nφ; and ii) means for generating two sets of DRRs, one set for each of said projections A and B; wherein each set includes DRRs that correspond to different combinations of said out-of-plane rotation angles, so that the number of DRRs in each set is N_(r)*N_(φ). 